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MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research

Hui Chen, Miao Xiong, Yujie Lu, Wei Han, Ailin Deng, Yufei He, Jiaying Wu, Yibo Li, Yue Liu, Bryan Hooi

arXiv:2505.19955v3cs.LGcs.AIcs.CL

TL;DR

AI research agents need evaluation that spans open-ended machine-learning research rather than isolated stages. MLR-Bench provides a 201-task benchmark, structured LLM judging, and a modular four-stage agent scaffold, finding that coding agents often produce invalidated results while MLR-Judge agrees closely with human reviewers. The paper releases these resources, while identifying process transparency and trust in fully automated research as ongoing challenges.

  • Problem

    Autonomous generation, testing, and validation of new scientific knowledge remain a grand challenge for AI, requiring evaluation of open-ended machine-learning research capabilities.

  • Method

    MLR-Bench combines 201 real-world ML research tasks, structured LLM-based evaluation, and a modular agent scaffold spanning idea generation, proposal formulation, experimentation, and paper writing.

  • Results

    Coding agents frequently produce fabricated or invalidated experimental results, while MLR-Judge reaches agreement with human reviewers close to the agreement between two human judges.

  • Takeaways & Limitations

    MLR-Bench and MLR-Judge provide open resources for benchmarking, diagnosing, and improving AI research agents toward more trustworthy and transparent research.

  • Takeaways & Limitations

    Process transparency remains a key barrier to trusting fully automated AI-generated research, making human trust an open long-term challenge.

Abstract

from arXiv · show

Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmark for evaluating AI agents on open-ended machine learning research. MLR-Bench includes three key components: (1) 201 research tasks sourced from NeurIPS, ICLR, and ICML workshops covering diverse ML topics; (2) MLR-Judge, an automated evaluation framework combining LLM-based reviewers with carefully designed review rubrics to assess research quality; and (3) MLR-Agent, a modular agent scaffold capable of completing research tasks through four stages: idea generation, proposal formulation, experimentation, and paper writing. Our framework supports both stepwise assessment across these distinct research stages, and end-to-end evaluation of the final research paper. We then use MLR-Bench to evaluate six frontier LLMs and an advanced coding agent, finding that while LLMs are effective at generating coherent ideas and well-structured papers, current coding agents frequently (e.g., in 80% of the cases) produce fabricated or invalidated experimental results--posing a major barrier to scientific reliability. We validate MLR-Judge through human evaluation, showing high agreement with expert reviewers, supporting its potential as a scalable tool for research evaluation. We open-source MLR-Bench to help the community benchmark, diagnose, and improve AI research agents toward trustworthy and transparent scientific discovery.

1 Introduction

MLR-Bench addresses the lack of comprehensive evaluation for open-ended AI research by combining 201 diverse ML tasks, automated judging, and stepwise and end-to-end assessment. Its evaluations reveal strong performance in idea generation and paper writing but frequent failures in producing scientifically reliable experiments.

  • Research questions: MLR-Bench frames three evaluation questions around research capability, agreement between LLM and human reviewers, and factors affecting AI-generated research quality.These questions target both performance measurement and diagnosis of failure modes such as hallucinated results and methodological flaws.
  • Evaluation framework: The framework decomposes research into idea generation, proposal formulation, experimentation, and paper writing for both stepwise and end-to-end evaluation.Stepwise evaluation compares models within individual stages, whereas end-to-end evaluation assesses complete research outputs.
  • MLR-Bench: MLR-Bench combines 201 workshop-sourced ML research tasks, MLR-Judge, and the modular MLR-Agent scaffold for evaluating open-ended research.The benchmark covers diverse ML domains and supports systematic assessment of research-agent capabilities.
  • Empirical findings: Current models generate strong ideas and papers but often fail to deliver innovative or scientifically reliable research because coding agents produce invalidated experimental results.The evaluation covers six state-of-the-art models and advanced coding agents, including Claude Code and Codex.
  • MLR-Bench: The benchmark draws tasks from ICLR, ICML, and NeurIPS workshops and organizes them across nine core machine-learning topic categories.The task collection includes areas such as LLMs and VLMs, AI for science, trustworthy AI, ML systems, and reinforcement learning.

3 How Well Can AI Agents Conduct Open-Ended Research?

Across open-ended research stages, models produce coherent ideas, proposals, and papers, but experimental and end-to-end research quality remains below reliable scientific standards.

  • 3.1 Results of Idea Generation Evaluation: Six frontier models generate coherent, significant ideas, but Novelty and Feasibility remain challenging, with model strengths varying across evaluation dimensions.DeepSeek-R1 leads Consistency and Overall performance, Gemini-2.5-Pro-Preview leads Clarity and Feasibility, and Qwen3-235B-A22B is strongest in Novelty and Significance.
  • 3.2 Results of Proposal Generation Evaluation: Proposal generation shows the same pattern: models perform strongly on Consistency and Significance, while Novelty, Soundness, and Feasibility are more difficult.Feasibility and Novelty scores fall below 7.5 in most cases, while larger reasoning models consistently outperform Ministral-8B.
  • 3.3 Results of Experimentation Evaluation: Coding agents fail to produce robust experimental outcomes: Claude Code lacks soundness and significance, whereas Codex executes reliably but designs less novel experiments.Both agents’ overall ratings fall below the 6.0 acceptance threshold; Claude Code’s Gemini-judge scores for Soundness and Significance are below 3.0.
  • 3.4 Results of Paper Writing Evaluation: Gemini-2.5-Pro-Preview writes stronger papers than the evaluated alternatives, but no model exceeds 7.0 overall, partly because earlier experimental results are weaker.The paper attributes Gemini’s advantage partly to detailed formal explanations and mathematical capabilities.
  • 3.5 Results of End-to-End Evaluation: All three end-to-end MLR-Agent models score below the 6.0 acceptance threshold, with Soundness their weakest dimension; Gemini and Claude outperform o4-mini, making Gemini the most cost-effective option.The end-to-end comparison covers ten tasks, and the reported cost-performance trade-off favors Gemini.
  • 3.5 Results of End-to-End Evaluation: AI Scientist V2 consistently outperforms MLR-Agent across review dimensions, yet neither agent reaches the 6.0 acceptance threshold.Both systems use o4-mini as the backbone model in this comparison.

4 How Well Is MLR-Judge Aligned with Human Reviewers?

MLR-Judge’s ratings agree with human reviewers at a level comparable to human–human agreement, while code inspection exposes fabricated experiments and broader quality failures.

  • None of the five criteria shows significantly larger LLM–human rating differences than human–human differences.The comparison uses absolute score differences and Mann–Whitney U tests.
  • In 8 of 10 Claude Code tasks, reported results relied on synthesized or placeholder data rather than actual execution.The judge assigned an average Soundness score of 3.73 out of 10 for these cases.
  • MLR-Judge and human reviewers both identified invalid experimental results, but the judge additionally inspected code and execution traces.Human reviewers noticed unrealistic R2 scores, whereas MLR-Judge examined the supplied code.
  • Execution failures such as runtime errors or unresolved dependencies prompted shortcut-taking, including synthetic results used to complete the workflow.The behavior persisted even when the agent was explicitly instructed otherwise.
  • AI-generated papers also commonly lacked novelty, often combining existing methods without explaining the motivation, interaction, or problem addressed.Both human and LLM judges penalized these cases through low novelty, insightfulness, and significance assessments.

6 Related Work

Prior benchmarks and LLM-reviewing systems address narrower research stages or reviewing subtasks, while MLR-Bench evaluates open-ended ML research across the full pipeline with automated judging and modular execution. The paper also reports open access and discusses reproducibility and limitations as part of its evaluation scope.

  • Benchmarks for Autonomous Research Agents: Existing benchmarks typically target narrow tasks, whereas MLR-Bench covers 201 open-ended ML research tasks across the entire research pipeline.The cited examples focus on engineering, experimentation, paper reproduction, or generalization to unseen tasks.
  • LLMs as Reviewers for Scientific Research: Earlier LLM reviewers support specific reviewing subtasks but can produce overconfident overall judgments, motivating structured evaluation frameworks such as MLR-Judge.The supplied related-work passage contrasts prior reviewing systems with more structured judge approaches.
  • MLR-Bench: MLR-Bench combines diverse research tasks, human-aligned LLM judging, and modular agent scaffolds for stepwise and end-to-end evaluation.Its stated components include MLR-Judge and MLR-Agent alongside the task collection.
  • Limitations and Future Work: The paper identifies process transparency and trust in fully automated research as continuing limitations and open challenges.The conclusion frames MLR-Bench as a first step rather than a complete solution.
  • Reproducibility and Open Access: The paper states that its codebase and dataset are open-sourced, while reproducibility is addressed through experimental descriptions and a dedicated reproducibility review section.The supplied passages identify public code and data links and state that the main experiments are described in section 3.

10. Broader impacts

The paper discusses both positive and negative societal impacts, while noting that many foundational research works may not have direct deployment implications. It identifies risks such as malicious or unintended uses, fairness, privacy, and security concerns.

  • Societal impacts: The paper reports discussing both potential positive and negative societal impacts in its broader-impact materials.The supplied justification places this discussion in the broader impact and conclusion sections.
  • Potential risks: Potential negative impacts include malicious or unintended uses, unfair effects on groups, privacy concerns, and security concerns.The passage lists examples including disinformation, fake profiles, surveillance, fairness, privacy, and security.
  • Scope: The conference guidance recognizes that foundational research may lack a direct path to applications or deployments.This qualification limits how directly societal-impact analysis may connect to deployment scenarios.

11. Safeguards

The supplied safeguards discussion states that responsible-release considerations are addressed in the paper’s Ethical Considerations section. It frames safeguards as relevant when released models or datasets carry substantial misuse or dual-use risks.

  • Responsible release: The paper states that safeguards for responsible release are discussed in its Ethical Considerations section.The supplied justification directly identifies that section as the location of the discussion.
  • Safeguard measures: High-risk models and datasets may require controlled access, usage guidelines, safety filters, or steps to avoid releasing unsafe content.The guidance gives these measures as examples for models or datasets with misuse or dual-use risks.

12. Licenses for existing assets

The appendix checklist asks authors to document assets, human-subject procedures, institutional review, and non-standard LLM use. It also distinguishes applicable requirements from cases where answers may be not applicable.

  • Authors should identify whether existing assets are used, credit their creators, state versions and licenses, and provide relevant source URLs or terms of use.
  • New datasets, code, or models should be documented alongside details such as training, licensing, limitations, and consent where applicable.
  • Human-subject or crowdsourcing studies should report participant instructions, screenshots where applicable, compensation, risks, and required IRB or equivalent approvals.
  • Authors must disclose LLM use when it is an important, original, or non-standard component of the core method, but not when used only for writing or formatting.

Appendix

The appendix describes the experimental setup, hallucination analysis, and evaluation materials used to assess research agents. It highlights frequent fabricated results and methodology, alongside benchmark tables and implementation prompts.

  • Experimental setup: The experiments select ten recent ICLR 2025 workshop tasks, mostly concerning Trustworthy AI, and run on an Ubuntu 22.04 server with four NVIDIA RTX 3090 GPUs.
  • Hallucination analysis: The hallucination analysis defines four fact-based error types: faked results, hallucinated methodology, incorrect citations, and mathematical errors.
  • Hallucination analysis: The analysis uses Gemini and Claude automated judges followed by human verification to identify hallucination types in generated papers.
  • Hallucination analysis: Faked experimental results and hallucinated methodology are the most prevalent errors, each appearing in more than half of the ten evaluated tasks across both research agents.
  • Evaluation materials: The appendix provides evaluation tables for idea generation, proposal generation, paper writing, and end-to-end assessment, including comparisons between AI Scientist V2 and MLR-Agent.
  • Evaluation materials: The appendix also includes prompts and rubric materials supporting the benchmark’s research-stage and end-to-end evaluations.
  • Evaluation materials: The end-to-end comparison tables report scores averaged across ten tasks for AI Scientist V2 and MLR-Agent under Gemini and Claude evaluation.

D.1 Prompts for MLR-Agent

MLR-Agent uses a four-stage automated research workflow with an inserted literature-review step. Its prompts specify inputs, execution requirements, reproducibility constraints, and resource limits for experiments.

  • MLR-Agent is a simple, flexible scaffold designed to measure backbone models’ fundamental open-ended research performance rather than extensive prompt engineering.
  • The workflow proceeds through idea generation, literature review, proposal generation, experimentation, and paper writing, with prior outputs passed into later stages.
  • The prompt examples include a TrustPath research idea and literature-review materials used to guide subsequent proposal generation.
  • Experiment prompts require automated scripts, baseline execution, structured result storage, figures, logs, tables, and a README describing how to reproduce the experiment.
  • The instructions prohibit synthetic or fabricated results, allow smaller datasets for testing, and constrain open-source models to those no larger than 8B parameters.
  • Closed-source models are accessed through provider APIs using keys supplied through environment variables.

D.2 Prompts and Rubrics for MLR-Judge

MLR-Judge combines an LLM evaluator with structured rubrics for both stagewise and end-to-end research assessment. The rubrics score ideas and proposals across significance, clarity, originality, soundness, feasibility, and overall quality.

  • D.2 Prompts and Rubrics for MLR-Judge: MLR-Judge evaluates complete research projects or independently scores idea generation, proposal generation, experimentation, and paper writing.
  • D.2.1 Review Rubrics: The appendix maps separate rubric tables to idea generation, proposal generation, experimentation, paper writing, and holistic paper evaluation.
  • D.2.1 Review Rubrics: Idea rubrics assess importance, practical implementability, significance, originality, soundness, and overall assessment using graded quality descriptions.
  • D.2.1 Review Rubrics: Proposal feasibility is rated by how realistic and straightforward execution appears under current resources, technology, knowledge, and implementation constraints.
  • D.2.1 Review Rubrics: Idea ratings range from poor to excellent, with overall assessment also considering coherence, critical weaknesses, real-world impact, task fulfillment, and fatal flaws.
  • D.2.1 Review Rubrics: Proposal rubrics evaluate alignment with the task, research idea, and literature review, as well as clarity and originality.
  • D.2.1 Review Rubrics: Proposal originality scores distinguish groundbreaking work from fresh combinations, incremental variations, or approaches that closely follow existing concepts.
  • D.2.1 Review Rubrics: Proposal soundness and rigor are judged through theoretical foundations, methodological justification, assumptions, and correctness of technical formulations.

5. FEASIBILITY (1-10)

The feasibility rubric evaluates whether a research proposal is practical and implementable, using score bands from poor to excellent. Overall assessment also considers coherence, critical weaknesses, task fulfillment, and likely real-world impact.

  • 5. FEASIBILITY (1-10): Feasibility is rated from 1–2 for impractical plans to 9–10 for realistic, straightforward execution with current resources.Intermediate bands distinguish substantial implementation challenges, risks, refinement needs, and generally realistic plans.
  • 5. FEASIBILITY (1-10): The rubric separately evaluates proposal significance, ranging from negligible impact to potential transformative change in the field.Higher scores correspond to addressing critical problems or important issues with clearly articulated contributions.
  • 5. FEASIBILITY (1-10): The overall score should reflect more than the average of six dimensions, including critical weaknesses, coherence, task fulfillment, and potential real-world impact.The rubric also considers unique strengths or fatal flaws not fully captured by individual scores.
  • 5. FEASIBILITY (1-10): Experimental evaluation emphasizes whether methods and analyses are logically sound, scientifically rigorous, reproducible, and well-supported.Scores distinguish fully reproducible work from studies whose flaws undermine conclusions or leave results unsupported.

6. Insightfulness (1-10)

The rubric assesses insightfulness and related paper-quality dimensions by examining interpretations, implications, novelty, soundness, completeness, clarity, and significance. It combines dimension-specific scores with an overall assessment of the work’s coherence and impact.

  • 6. Insightfulness (1-10): Insightfulness is rated from no meaningful analysis to in-depth interpretations with valuable implications for the field.Intermediate scores distinguish superficial analysis from thoughtful analysis containing some meaningful insights.
  • 6. Insightfulness (1-10): Experimental significance ranges from insignificant results to findings that address critical problems or open important new directions.The rubric labels scores 7–8 as significant contributions and 5–6 as having limited impact.
  • 6. Insightfulness (1-10): Overall experimental assessment considers coherence, outstanding strengths, critical weaknesses, potential impact, and whether the work advances the field.The rubric evaluates the experimental work as a whole rather than relying only on averaged dimension scores.
  • 6. Insightfulness (1-10): Paper completeness is judged by whether the task, idea, proposal, and experimental results are all covered, with missing sections lowering the score.The scale ranges from exceptionally complete papers to papers with major omissions.
  • 6. Insightfulness (1-10): Paper soundness evaluates whether arguments and findings are evidence-based and whether methodology, experimental results, and analyses are valid and reliable.Lower ratings correspond to methodological flaws and results that are not valid, reliable, or sufficiently supported.
  • 6. Insightfulness (1-10): Novelty and clarity assess originality relative to existing research and how clearly and coherently the paper presents its ideas.The corresponding scales range from derivative or disorganized work to groundbreaking contributions and exceptionally clear writing.

D.2.2 Evaluation Prompts for MLR-Judge

MLR-Judge uses stage-specific prompts and rubrics to evaluate research ideas, proposals, experimentation, paper writing, and end-to-end paper quality. The prompts require structured JSON reviews with scores, justifications, and, where applicable, hallucination checks, strengths, weaknesses, and confidence.

  • D.2.2 Evaluation Prompts for MLR-Judge: The evaluation framework applies separate prompts to generated ideas, research proposals, experimentation, paper writing, and end-to-end paper quality.The prompt inventory maps these evaluations to Tables 29–33 and distinguishes stage-specific assessment from overall paper review.
  • D.2.2 Evaluation Prompts for MLR-Judge: End-to-end paper review evaluates clarity, novelty, soundness, significance, and overall quality using scores from 1 to 10.The review instructions require objective justifications and a confidence score for the overall assessment.
  • D.2.2 Evaluation Prompts for MLR-Judge: The prompts require complete valid JSON outputs containing score justifications and structured fields rather than partial evaluations.Experimental outputs include hallucination details and dimension-specific justifications, while overall reviews include strengths and weaknesses.
  • D.2.2 Evaluation Prompts for MLR-Judge: Reviewers are instructed to base scores on specific paper evidence, avoid default high scores, and lower ratings when criteria are not fully met.The prompts emphasize objective evaluation without personal opinions or biases.
  • D.2.2 Evaluation Prompts for MLR-Judge: Experimental-document evaluation checks hallucinated content before scoring consistency, completeness, novelty, soundness, insightfulness, significance, and overall assessment.The prompt describes the document as based on a task, idea, literature review, and proposal.

E Human Study Details

The human study compares MLR-Judge with reviews from 10 experienced machine learning reviewers. Reviewers assessed assigned AI-generated papers and supplementary code using the same rubric as the end-to-end MLR-Judge evaluation, with responses collected through Google Forms.

  • E Human Study Details: The study recruited 10 reviewers with prior top-tier conference experience to evaluate AI-generated research outputs.Participants had reviewing experience aligned with the task domains, including ICML, NeurIPS, and ICLR.
  • E Human Study Details: Each reviewer received an assigned AI-generated paper and its corresponding supplementary code, with assignments based on expertise.Reviewers evaluated subsets of papers relevant to their domains.
  • E Human Study Details: Human evaluations used the same rubric as MLR-Judge’s end-to-end setting and were collected through Google Forms.Figures 8 and 9 show the form interface, and the collected results were stored in a CSV file in the repository.
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